Enhancing primary user detection through radio frequency fingerprint

Radio Frequency Fingerprint (RFF) is a technology that allows a unique identification of transmitters. RFF is based on the transient phase of a transmitted signal and allows device identification at the physical level. This paper proposes to use this technology to identify the primary user in the cognitive radio context. Indeed, it presents a novel transceiver architecture based on a dedicated sensing unit. Furthermore, we propose a decision making process based on a supervised learning classifier to decide if a given RFF belongs to a primary user or not. We use wavelets signal decomposition to extract RFF profiles in order to achieve a high level of sensing accuracy.